Book Image

Python Parallel Programming Cookbook - Second Edition

By : Giancarlo Zaccone
Book Image

Python Parallel Programming Cookbook - Second Edition

By: Giancarlo Zaccone

Overview of this book

<p>Nowadays, it has become extremely important for programmers to understand the link between the software and the parallel nature of their hardware so that their programs run efficiently on computer architectures. Applications based on parallel programming are fast, robust, and easily scalable. </p><p> </p><p>This updated edition features cutting-edge techniques for building effective concurrent applications in Python 3.7. The book introduces parallel programming architectures and covers the fundamental recipes for thread-based and process-based parallelism. You'll learn about mutex, semaphores, locks, queues exploiting the threading, and multiprocessing modules, all of which are basic tools to build parallel applications. Recipes on MPI programming will help you to synchronize processes using the fundamental message passing techniques with mpi4py. Furthermore, you'll get to grips with asynchronous programming and how to use the power of the GPU with PyCUDA and PyOpenCL frameworks. Finally, you'll explore how to design distributed computing systems with Celery and architect Python apps on the cloud using PythonAnywhere, Docker, and serverless applications. </p><p> </p><p>By the end of this book, you will be confident in building concurrent and high-performing applications in Python.</p>
Table of Contents (16 chapters)
Title Page

Understanding GPU programming

GPUs have become increasingly programmable. In fact, their set of instructions has been extended to allow the execution of a greater number of tasks.

Today, on a GPU, it is possible to execute classic CPU programming instructions, such as cycles and conditions, memory access, and floating-point calculations. The two major discrete video card manufacturers—NVIDIA and AMD—have developed their GPU architectures, providing developers with related development environments that allow programming in different programming languages, including Python.

At present, developers have valuable tools for programming software that uses GPUs in contexts that aren't purely graphics-related. Among the main development environments for heterogeneous computing, we have CUDA and OpenCL.

Let's now have a look at them in detail.